I got confused with how to do data partition that reflects in sample and out of sample forecast when I do time series forecasting in neural network.
What I understand is we have to divide data into: - training data set --> to estimate the model - validation data set --> to generalize and avoid overfitting - test data set --> to see model's performance
Does it mean that: - in sample forecasting: training and validation? if so, which performance should I see as in sample performance, the training MSE or the validation MSE? - out of sample forecasting = test data set?